PROJECT SUMMARY/ABSTRACT Mild traumatic brain injury (mTBI) is a major health problem in children, leading to approximately 600,000 emergency department visits each year in the United States. However, there is limited evidence guiding the management of the 14% of children with mTBI that have intracranial injury identified on computed tomography imaging. Accurate risk stratification of these patients is of paramount importance to ensure appropriately close clinical attention and intensive care unit (ICU) monitoring for high-risk children, while avoiding the psychological harm and substantial resource utilization associated with avoidable ICU admission. Several lines of evidence suggest that current practice based on physician gestalt is not evidence-based, may place some children at risk of harm, and is associated with high rates of avoidable ICU admission. To address this evidence gap, the Applicant, Dr. Jacob Greenberg, recently utilized a large multicenter dataset to develop and internally validate the Children's Intracranial Injury Decision Aid (CHIIDA) score to stratify the risk of neurological decline in these patients. However, spontaneous uptake of risk models is rare, reflecting a lack of external validation and focused implementation studies. Recognizing these challenges, this application proposes a hybrid effectiveness- implementation study to both externally validate the CHIIDA score using a modern multicenter dataset and study its implementation into the electronic health record. This two-year F32 postdoctoral fellowship will pursue these objectives through the following specific aims: 1) externally validate the CHIIDA model for managing children with mTBI and intracranial injury; 2a) define the sociotechnical context for implementing electronic clinical decision support in the care of children with mTBI and intracranial injury; and 2b) employ human-centered design to develop and evaluate a prototype electronic clinical decision support tool for risk-stratifying children with mTBI and intracranial injury. This study supports the Agency for Healthcare Research and Quality's (AHRQ) mission of implementing usable, evidence-based clinical decision support into the electronic health record to improve the safety and quality of healthcare delivery. Consistent with this goal, this study's results will ultimately lead to a cluster- randomized trial of electronic clinical decision support based on the CHIIDA model. At the same time, the proposed training plan will provide the Applicant with valuable skills in qualitative research methods, implementation science, biomedical informatics, and risk model validation. Consequently, this F32 postdoctoral fellowship will support the Applicant's goal of becoming an independent health services researcher committed to developing and implementing evidence-based programs to improve healthcare safety and quality.